Dive Brief:

Rapid digitization of health information in EHRs and other repositories is creating new opportunities for AI in healthcare, but challenges in data accessibility, privacy and security persist, according to a new ONC report.

Frustration with legacy medical systems, the omnipresence of networked smart devices and consumer comfort with at-home services offered by Amazon and other tech vendors is driving interest in AI's potential.

Smartphone, social and environmental data can all be potential sources to fuel AI's use in healthcare. However, the report concludes such data must be high quality and reliable. Otherwise, AI's promise will not be realized in healthcare.

Dive Insight:

AI is a hot healthcare topic but still needs to be translated into reality, especially in an industry as complex as healthcare.

During the second quarter of 2017, CB Insights counted 29 investment deals in the healthcare AI space — a record number — and predicted 2017 would set a six-year high.

Enthusiasm is expected to stay heated into 2018, with demand for tools that go beyond noting social determinants of health to using that data to inform patient care plans.

While investors will continue to fund wearables and biosensors, what grabs their attention are specific clinical use cases these technologies can support, Megan Zweig, director of research at Rock Health, told Healthcare Dive recently.

Tech giants including IBM Watson, Microsoft, Google and Apple are staking a claim in the space, too. Last month, Google launched Deep Variant, an open-source tool that uses AI to create a picture of a person’s genetic blueprint using sequencing data. The goal is to pinpoint specific genes or gene mutations that can help providers better manage disease states.

But challenges to widespread use of AI in health remain, as the ONC study shows. Among these are the acceptance of AI applications in clinical practice, difficulty leveraging divergent personal networked devices and AI solutions, access to quality training data on AI applications in health and gaps in data streams.

The report belies a large obstacle for rampant AI use. White noting the importance of high quality and reliable data, the industry has a data standards problem at the moment which needs to be ironed out.

Currently, different vendors and clinicians send unstructured data in medical records back and forth across EHR systems through continuity-of-care documents, which are format flexible. If the promise of AI relies on reliable data, standards will have to be well-defined to ensure the data are high quality.

On the bright side, the industry seems aware that healthcare is close to a breaking point at interoperability. The growing Internet of Things and consumerism in healthcare naturally demands a more networked, connected industry approach.

CMS Administrator Seema Verma in a town hall webcast on Wednesday with American Hospital Association CEO and President Rick Pollack said interoperability will be a topic of interest for the agency. She told listeners they will hear more from CMS in the future.

Technology is changing every industry in significant ways. To help frame how, I’m starting a new series discussing top trends in various markets. First up: health care.

No one can dispute technology’s ability to enable us all to live longer, healthier lives. From surgical robots to “smart hospitals,” the digital transformation is revolutionizing patient care in new and exciting ways. That’s not all. National health expenditures in the United States accounted for $3.2 trillion in 2015—nearly 18% of the country’s total GDP. It’s predicted that the digital revolution can save $300 billion in spending in the sector, especially in the area of chronic diseases. Clearly there is value—human and financial—in bringing new technology to the health care market. The following are just a few ways how.

Telemedicine

Even back in 2015, 80% of doctors surveyed said telemedicine is a better way to manage chronic diseases than the traditional office visit. Why? Telemedicine offers patients and health care providers both a new wave of freedom and accessibility. For the first time, a patient’s care options are not limited by geographic location. Even patients in remote areas can receive the highest quality of care, providing they have an internet connection and smart phone. Telemedicine can also save both time and money. Patients no longer have to schedule their days around routine follow-up visits (and long office waits). Instead, they can hop on a conference call to get the prescription update or check-up they need.

Nowhere has telepresence been more useful than in the mental health field. Now, those seeking emotional support can find access to a therapist or counselor at the click of a button, often for far less than they would pay for a full office visit. Internet therapies, for instance, “offer scalable approaches whereby large numbers of people can receive treatment and/or prevention, potentially bypassing barriers related to cost, location, lack of trained professionals, and stigma.” Telemedicine makes it possible.

Mobility And Cloud Access

Have you ever played phone tag with your doctor while waiting for important test results? It’s so nerve-racking! That’s why mobility and cloud access have been such a tremendous help in increasing accessibility for patients and doctors alike. By 2018, it’s estimated that 65% of interactions with health care facilities will occur by mobile devices. Some 80% of doctors already use smartphones and medical apps, with 72% accessing drug info on smart phones on a regular basis. Gone are the days of paper charts and file rooms. Hospitals, insurance companies, and doctor’s offices are now storing patient medical records in the cloud, with patients able to access test results online 24/7.

Given HIPAA laws relating to patient privacy, it’s probably no surprise this has also led to an increased focus on data protection and security. According to one report, “the black-market value of medical data is greater than even that of financial information.” Believe me when I say: No industry is more focused on virtualization security right now than health care.

Wearables And IoT

I remember the days when going into the local grocery store and getting my blood pressure read at one of those prehistoric machines seemed exciting. Imagine: A machine that helped me manage my own well-being without setting foot in a doctor’s office. Now, mobile devices as small as my cell phone can perform ECGs, DIY blood tests, or serve as a thermometer, all without even leaving my house. With help from automation, patients can even be prompted to check their weight, pulse, or oxygen levels, and enter results into mobile patient portals. Even better: They can transmit the results to my doctor in real time. Those details, when entered regularly, can help predict one’s risk for heart disease and other illnesses, ultimately saving lives. This is far more than cool. It’s life-saving.

Artificial Intelligence And Big Data

Big data is king in the digital world, and health care is no exception. Yes, it can be gathered to measure customer satisfaction. But perhaps more importantly, it can be used to automatically identify risk factors and recommend preventative treatment. Even more exciting: with the rise of the Internet of (Medical) Things (IoMT), mobile and wearable devices are increasingly connected, working together to create a cohesive medical report accessible anywhere by your health care provider. This data is not just useful for the patient. It can be pooled and studied en masse to predict health care trends for entire cultures and countries.

Empowered Consumers

All of the above have led to an entirely new trend in healthcare: patient empowerment. While many of us have come to associate health care with high costs and long waits, patients are now in the driver’s seat, with better access to higher-quality doctors, and higher satisfaction rates overall. It’s a healthy new way to look at health care, and one that holds promise for all of us with easy access to the digital landscape. My blood pressure is already lowering just imagining the possibilities.

Artificial Intelligence has made its way to every field possible, steamrolling the processes along its way. One such field is healthcare. They say healthcare is a field that is not very rules based and a successful doctor is the one who leverages his/her experience to deal with complex and unseen cases. However, there are many low hanging fruits that are already being plucked by AI. This trend is being fueled by increasing digitization in healthcare data and advances in new algorithms. In this piece, we intend to give you a sneak peek into how AI is leading to improved healthcare for humanity. Below are some key examples of research areas and applications.

Virtual Slides Diagnosis

The tissue-based diagnosis has seen technological advancement with the introduction of virtual slides. However, virtual slides demand a lot of time and efforts than that for viewing the original glass slides from the pathologists. This is the time taken in the selection of information containing fields of view. Artificial intelligence can automate the tissue diagnosis routine work. Deep Convolutional Neural Networks are already being used in this area. Automated diagnosis would save a lot of time wasted in supervising and the pathologists can focus on the serious cases.

Diabetic Retinopathy Treatment

Diabetic Retinopathy (DR) is the fastest growing cause of blindness, with nearly 415 million diabetic patients at risk worldwide. If not caught early, it can lead to irreversible blindness. In “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs“, published by JAMA, Google presented a deep learning algorithm capable of interpreting signs of DR in retinal photographs, potentially helping doctors screen more patients in settings with limited resources.

Skin Cancer Treatment

Sebastian Thrun’s lab at Stanford released an AI algorithm which detects Skin Cancer with very high accuracy. This algorithm was tested against 21 board-certified dermatologists. In its diagnoses of skin lesions, which represented the most common and deadliest skin cancers, the algorithm matched the performance of professional dermatologists.

Medical Diagnosis

AI algorithms can aid doctors in medical diagnosis.They can highlight key instances in a person’s previous health history. Incorporating AI into the medical field has the potential to change and vastly improve healthcare in its core. From improved diagnostic accuracy to better-optimized treatment plans, AI could be the key to better medical care for doctors and patients alike.

In August 2016, doctors at a hospital in Japan misidentified a 60-year-old woman’s leukemia. But IBM’s Watson examined a vast database of 20 million research papers and made a successful diagnosis in just 10 minutes. The AI-based system can be utilized to prune out the irrelevant data and help the doctor think more clearly focusing on the vital data.

Risk Prediction

The team of primary care researchers and computer scientists compared a set of standard guidelines from the American College of Cardiology (ACC) with four ‘machine-learning’ algorithms. These algorithms analyzed large amounts of data and self-learn patterns within the data to make predictions on future events which were a patient’s future risk of having heart disease or a stroke, in this case.

The results, published in the online journal PLOS ONE, showed that the self-teaching ‘artificially intelligent’ tools were remarkably more accurate in predicting cardiovascular disease than the established guidelines. This technology is a godsend for insurance companies by helping them do a more effective appraisal of health risks of a customer.

Radiology

Applying AI for Radiology is harder as compared to Histopathology and hence we are yet to see groundbreaking results here. There is, however, a lot of work going on in situations where X-rays, CTs, and MRIs can be analyzed automatically, thereby giving radiologists a quick second opinion to consult with.

AI has already been used for Chest X-rays for direct diagnosis. Some of the other areas where AI aids diagnosis significantly is segmenting hip bones and lumbar vertebra for QCT/MRI in osteoporosis screening.

A Recent release of Stanford Medical-ImageNet is likely to start a revolution like what ImageNet did for normal images.

Automating Drug Discovery

Discovery of a new drug takes years of research, its launch takes even more time and money. Automating drug discovery through AI can tremendously reduce the cost and time as well.The average biomedical researcher deals with a huge amount of new information every day. It is estimated that the bioscience industry is getting 10,000 new publications uploaded on a daily basis from across the globe and among a huge variety of biomedical databases and journals. So, it becomes impossible for the researcher to process the entire information alone. Artificial Intelligence has a vital role to play in elevating the work of drug development researchers.

A study published in Cell Chemical Biology reveals a big data-based approach to detecting toxic side effects of a drug before it goes to the expensive clinical testing. In the approach called PrOCTOR, researchers analyze each drug using 48 different features to ascertain its safety for clinical use. The entire process is automated using machine learning.

A company named BenevolentBio has been doing research into Amyotrophic Lateral Sclerosis (ALS). The AI they’ve developed incorporated in the company’s Judgement Correlation System (JACS) reviews billions of sentences and paragraphs from scientific research papers and abstracts. JACS then links direct relationships between the data and regulates the data into ‘known facts’. These known facts are used to generate a large number of possible hypotheses using criteria set by the scientists. Based on these hypotheses, possible drugs are discovered. They have already managed to identify two potential drug targets for Alzheimer.

Imagine you are in an open field, the sun shines on you, with the bees humming softly in the air. How far can you see and hear? When it comes to vision, it’s around 50 miles, talking about hearing, it’s only 1-2 miles at best! What about the smell of the flowers? Without the wind blowing, only 10-20 meters. How about touching or tasting? Well, it depends on your arm’s length, but obviously not further than that. And the same goes for your tongue and tasting.

It is almost a cliché to emphasize the importance of the eyes and vision, but it’s a luminous example to illustrate how the eyes are our most important sensory organ. Hence, if you catch an eye disease or have to face a serious eye condition, you feel very motivated to get better immediately.

Eye conditions affect way too many people worldwide

The International Agency for the Prevention of Blindness estimated in 2015 that 36 million of people are blind, and 217 million people suffer from moderate or severe distance vision impairment. It is a hopeful tendency that the prevalence of blindness and vision impairment combined has dropped from 4.58% in 1990 to 3.37% in 2015. The decrease can be attributed to progress in technology, for example in surgical techniques and concerning treating eye infections.

Yet, digital health still has a lot to do for lessening plenty of suffering which comes from not being able to see the world clearly. In the last couple of years, it actually started to undertake the task to transform the field of ophthalmology, offering its innovative solutions for the broadest spectrum of eye conditions. Treating less serious ailments gets faster, more targeted and more efficient, while the means for curing more serious and life-altering illnesses improve. Here, I outlined the way technology delineates for the future of eye care and vision.

With “bionic eyes” for reversing blindness

Disruptive technologies gave a huge boost to the creative minds of ophthalmology. Types of conditions causing blindness, such as AMD or retinitis pigmentosa, an inherited eye condition causing loss of sight gradually and causing blindness for an estimated 1.5 million people worldwide, have been treated successfully with mind-blowing innovations.

The California-based firm, Second Sight, the German company, Retina Implant AG, and French venture, Pixium Vision, develops implantable visual prosthetics to restore vision to patients who are blind as a result of the rare condition of retinitis pigmentosa. In 2016, The Guardian reported that a blind woman suffering from it was fitted with the implant labeled “bionic eye” in the UK as part of a trial at the Oxford Eye Hospital. She has spoken of her joy after she was able to tell the time for the first time in more than six years. That must have been truly amazing!

In 2015, surgeons in Manchester, UK have performed the first bionic eye implant for an AMD patient using Second Sight’s innovation. The 80-year-old Ray Flynn lost entirely his central vision, but with the help of the retinal implant, he could make out shapes on the computer screen. Researchers say that the implant cannot provide any highly detailed vision – but it can help patients detect distinct patterns such as door frames and shapes.

Brain implants and artificial retina instead of bionic eyes?

As Second Sight’s current Argus II device for helping people with retinitis pigmentosa only restore minimal vision and cost $150,000, they only sold 250 of them so far. A while ago, the company started to develop a modified version of its innovation, which completely leaves the eye out of the procedure and instead mobilizes the part of the brain responsible for processing visual information, the visual cortex. Delivering electrical pulses here should tell the brain to perceive patterns of light. The company hopes that this new innovation could help about 6 million people in the future who are blind due to other causes, like cancer, diabetic retinopathy, glaucoma, or trauma. The company hopes to begin enrolling patients for trials in October and do its first implant by the end of the year. We can’t wait to know more about it!

Instead of “bionic eyes” that stimulate brain cells with lights coming from a tiny video camera or stimulate the visual cortex directly through electrodes, the Italian Institute of Technology has developed a new approach for treating retinal degeneration, with a prosthesis implanted into the eye that serves as a working replacement for a damaged retina – basically an artificial retina. Their research showed promising results for lab rats, and they plan to carry out the first human trials in the second half of 2017 and gather preliminary results during 2018.

The miraculous CRISPR and other gene therapies for regaining vision

CRISPR-Cas9 or as used in plain language, CRISPR, the breakthrough gene editing method, has already shown its potential future use in eye care. Experts even say the eye is an ideal place to start for the first clinical use of CRISPR. Compared to other parts of the body, the eye is easy to access for surgery, readily accepts new tissue and can be noninvasively monitored.

Scientists at Columbia University Medical Center and the University of Iowa used CRISPR to repair a genetic mutation responsible for retinitis pigmentosa in induced pluripotent stem cells derived from a patient with the disease. The team reported a 13 percent success rate at converting the mutated gene variant into the normal one which is way better than previous studies. In February 2017, experts at the Center for Genome Engineering, within the Institute for Basic Science (IBS) reported the use of CRISPR in performing “gene surgery” in the layer of tissue that supports the retina of living mice. After the intervention, the mice showed signs of improvement from AMD. It is a ground-breaking experiment suggesting that CRISPR can not only be used to correct mutations causing hereditary diseases but also in the case of non-hereditary degenerative diseases.

Beyond CRISPR, other gene therapies also have a great chance to become a common treatment method for specific eye conditions in the future. Early October 2017, the FDA’s advisory panel approved a gene therapy called Luxturna, which targets a rare condition called Leber congenital amaurosis. Thus, the treatment method got one step closer to full FDA approval. The agency will make its final decision by next January. If the verdict is positive, the gene therapy will be the first approved treatment in the US to correct an inherited genetic trait – but it might be followed very soon by much more.

Eye care patients will also become the point of care

With the advancement of smartphones and other smart gadgets at lightning speed, it is only a matter of time before portable devices will appear on a large scale in ophthalmology as well. The tiny, well-designed and connected instruments and the accompanying apps make it possible to undertake eye examinations anywhere in the world – making patients the point of care.

For example, Peek Retina is the flagship product of Peek Vision, a UK-based company and foundation, a portable ophthalmoscope that enables you to view and capture retinal images on your smartphone wherever you are. The venture also offers smartphone-based vision eye tests, e.g. for measuring visual acuity. It greatly helps physicians in remote areas such as Sub-Saharan Africa diagnose and treat patients.

The MIT-spinout company, EyeNetra developed a diagnostic device for signaling refractive errors fast and accurately. The device, called Netra, is a plastic, binocular-like headset to be used with an app which calculates the difference between what the user indicates as “aligned” and the actual alignment of various patterns. This signals any refractive errors, such as near-sightedness, farsightedness, and astigmatism. The app then displays the refractive powers, the axis of astigmatism, and the pupillary distance required for eyeglasses prescriptions. EyeNetra will make school or workplace eye examinations in the future a lot easier than today.

Are 3D printed and digital contact lenses the future?

Digital contact lenses sound as science fiction: the translucid layer on your eye transmitting special information about your body to an outside device. Yet, it might be a reality soon. For example, Google teamed up with Novartis, to produce digital, multi-sensor contact lenses which are designed to be able to measure blood sugar levels.

Google and Novartis said the lens would contain a tiny and ultra slim microchip that would be embedded in one of its thin concave sides. Through its equally tiny antenna, it would send data about the glucose measurements from the user’s tears to his or her paired smartphone via installed software. Originally, the companies promised to put the digital contact lens around 2020 on the market. However, in March 2017 Novartis Chairman Joerg Reinhardt talked down the chances of the project bringing visible results in the next couple of years. I truly hope this is just a temporary setback.

Meanwhile, researchers at the University of Washington have created a contact lens with an LED display built into it – with the help of a 3D printer! While it is really difficult to manufacture a contact lens, which is one-third of a millimeter in diameter, a 3D printer sandwiches together different layers of interacting material, which makes it easier to place together so tiny pieces. While it was only an experiment, the research has important implications to improve the display technology of small devices. Maybe Google will 3D print the next generation of digital contact lenses, who knows?

Healing the eye faster

Innovation in regenerative medicine is flourishing: dentistry, dermatology, ophthalmology. A few specialties which can take pride in healing injured or diseased body parts faster and in a more efficient way.

For example, researchers in Turkey developed a regenerative medicine, that can heal the front of the eye in as little as two days after surgery. The drug called Cacicol stimulates faster tissue repair, appears to relieve eye pain, burning, and light sensitivity following an invasive intervention. Scientists treated with Cacicol patients suffering from a rare disease called keratoconus who went through a surgery known as corneal cross-linking. The drug helped decrease the initial healing from 5 days.

Artificial Intelligence for detecting eye conditions in time

Image recognition algorithms have the capacity to transform diagnostics based on medical imaging. In 2016, Google developed an eye-scanning technique for looking at retinal images and detecting diabetic retinopathyas well as a trained ophthalmologist. The disease is quite common among diabetes patients, and if it is not spotted early enough it may cause blindness. The machine-learning algorithm uses Google’s method for labeling millions of Web images as it examines photos of a patient’s retina to spot tiny aneurisms indicating the early stages of diabetic retinopathy. A year later, the search giant announced they have begun working on integrating the technology into a chain of eye hospitals in India.

Google is not the only one working on A.I. solutions for eye care, though. A teenage girl from India, whose grandfather in India was diagnosed with diabetic retinopathy, developed a smartphone app that can screen for the disease with the help of a specially trained artificial intelligence program and a simple 3D-printed lens attachment. A truly disruptive innovation: smart, cheap and potentially life-changing!

Eye conditions through augmented reality

Patient education is key in prevention and it also gives the best chance for physicians arriving at the most accurate diagnosis based on their patients’ explanation of their symptoms.

The use of Orca Health’s EyeDecide could bring exactly this result. The innovative, Utah-based mobile software company’s medical app uses an augmented reality camera display for simulating the impact of specific conditions on a person’s vision. EyeDecide can fully demonstrate the consequences of cataract or AMD and thus help patients understand their actual medical state.

Cyborgization is upon us?

I’m hopeful that eye conditions, visual impairment, and blindness will be entirely treatable in the future, even if that would mean their replacement with fully capable technologies. I believe the biggest ethical challenge of eye implants or devices replacing visual functions could be that it might facilitate cyborgization.

What if healthy people would like to live as Neil Harbisson? As someone whose vision is extended through an external technology? What if the average user will ask for bionic eyes as it does not get tired, you can zoom with it, browse and search online, even take photos that no one else could see?

Neil Harbisson is actually an artist born with achromatopsia or extreme colorblindness meaning he could only see in black-and-white. Harbisson received a specialized electronic eye, his “eyeborg” to be able to render perceived colors as sounds on the musical scale. He is capable of experiencing colors beyond the scope of normal human perception: Amy Winehouse is red and pink, while ringtones are green. In his view, cyborgization might start with a third eye on the back of the head or an implanted sensor indicating whether there is a car behind you.

If you are entirely freaked out by now, I have to tell you, we are rather far from implanting third eyes into people. However, we have to start to contemplate about the possibilities of such scenarios as we will arrive at the boundaries of privacy, ethics – and the ultimate merging of the human body with technology. We have to be ready for that!

All these terms fly around in IT organizations today as CIOs, battling marketplace uncertainties and cost pressures, look for ways to enhance enterprise performance. As with most technology trends, the hype tends to overhang reality by a significant margin in the early stages of adoption, much in line with Gartner’s hype cycletheory.

Early this year, I wrote a piece that discussed how emerging technologies such as artificial intelligence (AI) and blockchain will drive precision medicine this year. Halfway into the year, the signs are that the use of AI technologies has definitely picked up momentum.

A recent study by consulting firm Accenture provides us some interesting data points. Artificial Intelligence or AI in healthcare is expected to grow more than 10x in the next five years, to around $ 6.6 billion, at a compounded rate of over 40%. AI represents a $150 billion savings opportunity for healthcare, across a wide range of applications: robot-assisted surgery, clinical diagnosis and treatment options, and operational efficiencies, to name a few. In my firm’s work with healthcare technology firms and enterprises, there is definitely a palpable excitement about the growing demand for AI in healthcare. Before unpacking what that means, it may be worthwhile defining some of the terms that are used interchangeably and synonymously with AI.

At the operating levels, autonomics and robotic process automation (RPA) refer to software that runs on pre-determined rules and eliminates the need for human intervention (a good example is fetching benefit eligibility information in a health plan or managing routine IT infrastructure operations). In many cases, these tools – sometimes referred to as “bots” – learn from patterns of requests and remediate/update their algorithms to respond in a more intelligent fashion over time. At higher levels of application, cognitive and AI systems aim to “mimic” humans in terms of reasoning and judgment based on techniques such as neural networks and Bayesian models that help these technologies come close to making decisions in a human-like manner. However, as IBM CEO Ginni Rometty points out, these techniques are more about augmenting human intelligence today, not replacing it (man and machine, not man vs. machine).

There is no doubt that these emerging technologies can transform healthcare. There is a rapidly growing body of use cases and successful applications of AI in operational and clinical areas. Here are a few examples of how AI technologies are currently being applied in the healthcare and life sciences sectors.

Health plans: There is considerable traction today applying RPA tools and AI technologies for improving productivity and efficiencies in health plans. By codifying workflow rules and enabling self-learning through ontological patterns and databases, these technologies are being used in areas such as provider data management, claim approvals and exception management, fraud detection, and customer service operations.

Health systems: AI and automation tools have found wide applications in a range of functions including revenue cycle operations, diagnosis and treatment, and population health management initiatives. IBM’s Watson Health engine, for example, has made significant strides in applying cognitive and AI technologies in the field of oncology and diabetic retinopathy, allowing the search and analysis of vast amounts of data and knowledge to provide clinicians with inputs for targeted intervention options.

Life sciences: Pharma companies have started successfully applying AI tools in clinical trial phases of new drugs by automatically generating content required for regulatory submissions and reviews. On the other side of the equation, these tools are being applied in pharmacovigilance for case intake and reporting on the adverse effects of drugs. There is increasing interest in the use of AI for improving efficiencies in supply chain operations.

Across all of these segments, there are several commonly used applications, an example of which is the use of AI technologies for IT infrastructure operations in detecting and remediating network errors and application failures. Another example is the use of AI in patient engagement programs, especially for managing chronic conditions such as diabetes through automated alerts and interventions based on analysis of real-time data gathered through intelligent devices and wearables.

As the use of AI technologies gains momentum, more use cases will surely emerge. As healthcare transitions from a fee-for-service to a value-based care era, the need for advanced technologies for everything from precision medicine to increased operational efficiencies and improved patient engagement will drive the adoption rates for these technologies. Many of these initial projects are in pilot phases, and in the broader context, there is a relatively small number of healthcare enterprises that are investing in these technologies and programs. That is par for the course for new technologies in any field. Mainstream adoption may be a bit further away, and in the current environment of policy uncertainty, many of the smaller enterprises are likely to be in wait and watch mode, choosing to stay with business as usual till there is some clarity.

To paraphrase the sci-fi writer William Gibson, the future is already here, only it is unevenly distributed. This may be the most accurate summary of AI in healthcare at this time.

Automation through AI, robotics or 3D-printing will make healthcare more efficient and more sustainable. These new digital technologies will improve healthcare processes resulting in the earlier and more efficient treatment of patients. It will eventually shift the focus in medicine from treatment to prevention. Moreover, medical professionals will get the chance to move from repetitive, monotonous tasks to the challenging, creative assignments.

AI has certainly more revolutionary potential than simply optimizing processes: it can mine medical records or medical images in order to come up with previously unknown implications or signals; design treatment plans for cancer patients or create drugs from existing pills or re-use old drugs for new purposes. But imagine how much time you as a GP would have if the administrative process would be taken care of by an AI-powered system. Your only task would be to concentrate on the patient’s problem! Imagine how much time you as a GP could spare if healthcare chatbots and instant messaging health apps would give answers to simple patient questions, which do not necessarily need the intervention of a medical professional!

She could have been a great doctor!

These were exactly the thoughts in my head when I was watching the movie Her for the second time. I was fascinated again about the scene in which the main character played by Joaquin Phoenix got his new, AI operating system and started working with it. I could not stop thinking about the ways I could use such an AI system in my life and how it actually could make me a better doctor.

Don’t get me wrong, I think empathy and great communication with patients can make a doctor better primarily, but as the amount of medical information out there is exponentially growing; as the time for dealing with patients and information is getting shorter, it is becoming humanly impossible to keep up with everything. If I could devote the time it takes now to deal with technology (inputting information, looking for papers, etc.) to patients, that would be a huge step towards becoming better.

Through the following 10 ways, AI could make me a better doctor.

1) Eradicate waiting time

You would think that waiting time is the exclusive “privilege” of patients and doctors do not have any free moment during their overpacked days. However, suboptimal health care processes not only result in patients sometimes waiting for hours in front of doctors’ offices but also medical professionals losing a lot of time every day waiting for something (a patient, a lab result, etc.). An AI system that makes my schedule as efficient as possible directing me to the next logical task would be a jackpot.

2) Prioritize my emails

The digital tsunami is upon us. Our inboxes are full of unread messages and it is an everyday challenge not to drown into the ocean of new letters. I deal with about 200 e-mails every single day. I try to teach Gmail how to mark an email important or categorize them automatically into social media messages, newsletters, and personal emails, it’s still a challenge. In Her, the AI system prioritized all the 3000 unread emails in a second. Imagine if we could streamline digital communication completely in line with our needs and if we could share and receive information more efficiently and more accurately without too much effort.

According to a recent report in the New Scientist, half a million people have professed their love for Alexa, Amazon’s intelligent personal assistant and more than 250,000 have proposed marriage to it. I have to say, I would probably do the same if it could organize my emails that way. (Also, if Scarlett Johansson were to be the voice of the assistant.)

3) Find me the information I need

I think I have mastered the skill of searching for information online using dozens of Google search operators and different kinds of search engines for different tasks, but it still takes time. What if an AI OS could answer my questions immediately by looking up the answer online?

More and more intelligent personal assistants, such as Siri on iOS or Alexa for Amazon lead us into the future, and there will be soon highly capable, specialized AI-powered chatbots also in the field of healthcare. Bots like HealthTap or Your.Md already aim to help patients find a solution to the most common symptoms through AI. Safedrugbot embodies a chat messaging service that offers assistant-like support to health professionals, doctors who need appropriate information about the use of drugs during breastfeeding.

4) Keep me up-to-date

There is too much information out there. Without an appropriate compass, we are lost in the jungle of data. It is even more important to find the most accurate, relevant and up-to-date information when it comes to such a sensitive area as healthcare. That’s why I started Webicina, which collects the latest news from the best, most reliable sources into one, easily manageable magazine.

On Pubmed, there are 23 million papers. If I could read 3-4 studies of my field of interest per week, I could not finish it in a lifetime and meanwhile millions of new studies would come out. I need an AI to process the pile of information for me and show me the most relevant papers – and we will get there soon. IBM Watson can already process a million pages in seconds. This remarkable speed has led to trying Watson in oncology centers to see how helpful it is in making treatment decisions in cancer care.

5) Work when I don’t

I can fulfill my online tasks (emails, reading papers, searching for information) when I use my PC or laptop, and I can do most of these on my smartphone. When I don’t use any of these, I obviously cannot work. An AI system could work on these when I don’t have any device in hand.

Imagine that you are playing tennis or doing the dishes at home when an important message comes in. With the help of an AI, you could respond to your boss without the need to touch any devices – a toned down version of Joaquin Phoenix’s AI, that arranged the whole publishing process of his book without the need for him to lift a finger.

6) Help me make hard decisions rational

A doctor must face a series of hard decisions every day. The best we can do is to make those decisions as informed as possible. I can ask people whose opinion I value, but basically, that’s it. Unfortunately, you would search the world wide web in vain for certain answers.

But AI-powered algorithms could help in the future. For example, IBM Watson launched its special program for oncologists – and I interviewed one of the professors working with it – which is able to provide clinicians evidence-based treatment options. Watson for Oncology has an advanced ability to analyze the meaning and context of structured and unstructured data in clinical notes and reports that may be critical to selecting a treatment pathway. So, AI is not making the decision per se but offers you the most rational options.

7) Help patients with urgent matters reach me

A doctor has a lot of calls, in-person questions, emails and even messages from social media channels on a daily basis. In this noise of information, not every urgent matter can reach you. What if an AI OS could select the crucial ones out of the mess and direct your attention to it when it’s actually needed.

Moreover, if you look at the patient side, you will see how long is the route from recognizing symptoms at home until reaching out to a specialist. For example, in the Hungarian county of Kaposvár, the average time from the discovery of a cancerous disease until the actual medical consultation about the treatment plan was 54 days. This alarming number has been drastically reduced to 21 days with the help of a special software and by optimizing patient management practices since November 2015. Imagine, though, what earthquake-like changes AI could bring into patient management if the usage of a simpler process management tool and follow-up system could halve the waiting time!

8) Help me improve over time

People, even those who work on becoming better at their job, make the same mistakes over and over again. What if by discussing every challenging task or decision with an AI, I could improve the quality of my job. Just look at the following:

97% of healthcare invoices in the Netherlands are digital containing data regarding the treatment, the doctor, and the hospital. These invoices could be easily retrieved. A local company, Zorgprisma Publiek analyzes the invoices and uses IBM Watson in the cloud to mine the data. They can tell if a doctor, clinic or hospital makes mistakes repetitively in treating a certain type of condition in order to help them improve and avoid unnecessary hospitalizations of patients.

9) Help me collaborate more

Sometimes I’m wondering how many researchers, doctors, nurses or patients are thinking about the same issues in healthcare as I do. At those times, I imagine that I have an AI by my side, which helps me find the most potential collaborators and invite them to work together with me for a better future.

Clinical and research collaborations are crucial to find the best solutions for arising problems, however, more often than not, it is difficult to find the most relevant partners. There are already efforts to change this. For example, in the field of clinical trials, TrialReachtries to bridge the gap between patients and researchers who are developing new drugs. If more patients have a chance to participate in trials, they might become more engaged with potential treatments or even be able to access new treatments before they become FDA approved and freely available.

10) Do administrative work

Quite an essential percentage of an average day of a doctor is spent with administrative stuff. An AI could learn how to do it properly and do it better than me by time. This is the area where AI could impact healthcare the most. Repetitive, monotonous tasks without the slightest need for creativity could and should be done by artificial intelligence. There are already great examples leaning towards this trend.

IBM launched another algorithm called Medical Sieve. It is an ambitious long-term exploratory project to build a next generation “cognitive assistant” with analytical, reasoning capabilities and a wide range of clinical knowledge. Medical Sieve is qualified to assist in clinical decision making in radiology and cardiology.

Many fear that algorithms and artificial intelligence will take the jobs of medical professionals in the future. I highly doubt it. Instead of replacing doctors, AI will augment them and make them better at their jobs. Without the day-to-day treadmill of administrative and repetitive tasks, the medical community could again turn to its most important task with full attention: healing.

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